The coastal area is an important territory for many reasons, e.g. as a recreational area, natural parks and reservoirs, protection against sea flood and storms. The last usage is especially crucial in the Netherlands, because the most densely populated areas are located just behind the coastal defense and are partly below the mean sea level. Therefore, it is essential to continuously monitor and maintain the coast in order to protect the Dutch hinterland from the sea. Since 1965 the Dutch Ministry of Transport, Public Works and Water Management (RWS, Rijkswaterstaat) annually acquire the JAarlijkse KUStmetingen (JARKUS) data. The cross-shore topography and bathymetry is measured along profiles 250m apart. The disadvantage is, that the JARKUS data are too sparse in time and space, to ensure sufficient observations of the dynamic coast environment. The time is especially important, when a high energy event occurs that can cause most dramatic changes. Therefore, a flexible system is needed that can access a damaged area immediately after the storm and enables (near) real-time processing of the captured data. To make right and timely decisions it is important that results, e.g. DTM, are available shortly after the acquisition itself. Besides, to estimate in detail the beach erosion caused by heavy storm events, high spatial resolution measurements are needed. According to the above described application and requirements the most appropriate acquisition technique and processing has to be chosen. Advances in the hardware and software technologies enable to directly acquire 3D models from real world environments [Grinstead et al., 2005]. Compared to the laser airborne system, i.e. ALS, which is commonly used by now to map the beach topography, the vehicle platform is considered to be more flexible. One of the potential techniques is the laser L-MMS. It is a complex real-time, multi-tasking and multi-sensor system. It integrates line scanners and optionally digital cameras for mapping, Global Navigation Satellite Systems (GNSS) for positioning and usually Inertial Navigation System (INS) to measure platform orientation. The measurements from those sensors are integrated and result in a 3D laser point cloud. In principle it is possible to quickly obtain 3D data of a large extended area with a high spatial resolution. On the basis of the mentioned properties, RWS decided to initiate a pilot-project using a laser L-MMS. The requirements for the acquired data are that the vertical accuracy of DTM is at least 10cm at a grid spacing of 1x1m and that this result is available close to the real-time. In this master thesis the level of obtainable quality of the laser L-MMS data and derived laser L-MMS DTM is investigated in detail. This evaluation is performed in three steps. First, an a-priory analysis is performed on the basis of the L-MMS geo-referencing model. This model includes L-MMS measurements of the three sensors and calibration parameters connecting those sensors. The result is 3D laser point cloud. By nature, the observations entering the geo-referencing model include errors and therefore effect the 3D laser point positioning quality. To estimate this quality a theoretical random error budget is constructed. It shows the relative components of random errors within the system and the expected quality of the laser point positioning. The specifications and estimated observations’ random errors of the L-MMS system called StreetMapper are considered in a practical random error budget computations. Results show that the GPS/INS positioning error impacts the 3D laser point position the most. The second biggest contributors are errors of the laser scanner measurements, i.e. errors in range and scanning angle. The range error effects more the horizontal and scan angle error the vertical positioning error. Because the StreetMapper GPS/INS positioning solution on the open beach area is relatively good, the further laser point quality improvements should consider laser scanners of higher precision. In the second analysis the theoretical model of the described pre-analysis is applied on the real L-MMS data. That is, the height precision due to the L-MMS measurement and calibration errors (measuring precision) is computed for each laser point. Besides, the intersection geometry of the laser beam with the relatively horizontal and flat beach is investigated. The scanning geometry is changing a lot over the beach and gets poorer further away from the trajectory. This means that the distance from the laser scanner to the target as well as the incidence angle increases. Those geometric attributes are teken into account to compute the so-called geometrical precision. Together with the measuring precision they define the theoretical precision of the laser point height. Besides, an empirical quality measure is obtained by a relative Quality Control (QC). The relative QC is here developed considering the land-based laser data advantage, like a high laser point density that results in overlapping footprints. Height differences between the two overlapping laser points, called identical points, are considered as the empirical quality measure. The comparison of the theoretical and empirical quality measures shows big differences. Most likely the theoretical precision is estimated too pessimistic, because the random errors of the measurements and calibration parameters are mostly overestimated. It is therefore recommended to first verify the theoretical error model by comparing the values obtained in this research with reference data of a higher quality. This way also the systematic errors could be verified, that are here assumed to equal zero. On the other hand, the assumptions on the identical points should be further tested. In the third analysis a laser L-MMS DTM is evaluated. DTM is the most important or in other words, the most widely used L-MMS product that should be provided to the end user together with a detailed quality description. This way, a DTM user can better trust and rely on the DTM height information. Here, the goal is to answer the RWS question, weather the DTM of 1x1m size can have a quality better than 10cm. The L-MMS and ALS dataset are used in this analysis. First, the L-MMS terrain laser points are divided into a 1x1m grid cells. A weighted Moving Least Squares (MLS) is performed for each grid cell to fit a tilted plane to its terrain points. The weight matrix is constructed from the individual theoretical laser point variances. This method is applied on the StreetMapper terrain points that lie on the sandy beach and part of the dunes. Resulting interpolated grid point heights do not differ much from the averaged height of those terrain points. Therefore it is concluded, that the simple averaging interpolation can be used to construct a laser L-MMS DTM on the Dutch beach. The resulting L-MMS DTM quality is highly dependent on the number of terrain points within a grid cell. Because the L-MMS point density is in general very high, those redundant data reduce the random errors of laser point positioning. Thus, the DTM has on average higher quality than the input laser data. It is concluded that the RWS requirement on the laser L-MMS DTM accuracy can be easily met for the beach and the dune area. Most of the grid cell on the beach have the height precision better than 1cm. To achieve similar accuracy also in the dune area, a measurement set-up needs to be changed in order to acquire more points. Thus, it is recommended that the vehicle drives closer to the dune foot and that the laser scanners are lifted higher above the ground. The comparison of L-MMS and ALS data shows the advantage of the L-MMS technique due the high laser point density. On the other hand ALS technique offers better data coverage on the coast. However, higher laser scanner platform might minimize the L-MMS data voids that occur behind elevated features in the pre-dune area. This way the completeness, reliability and the quality of the laser L-MMS DTM DTM could be improved.